Positron emission tomography (PET) is used for a variety of applications, ranging from clinical oncology and cardiology to pre-clinical drug discovery and neuroscience. Virtually all PET applications rely on the same workflow, which involves reconstructing one or more images representing the time-varying distribution of a radiotracer. These images can later be processed and analyzed to extract biologically relevant parameters such as standardized uptake value. Increasingly, however, PET is used in “tracking” applications, for which the “imaging” paradigm may not be optimal.
Cell tracking is a method that involves labeling cells ex vivo using a contrast agent and imaging their time-varying distribution in vivo. Clinically, the most common use of cell tracking is for tracking white blood cells to identify occult sites of infection or inflammation. More recently, advances in stem cell science and immunology have led to new cell-based therapies for cardiac, neural, and pancreatic tissue regeneration and cancer immunotherapy. Cell tracking is also widely used as a preclinical research tool to study biological processes such as cancer metastasis. Transplanted cells can be labeled and imaged non-invasively using magnetic resonance imaging (MRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and optical imaging. So far, no consensus has been reached on which imaging modality is best suited for cell tracking MRI is the only imaging modality that has shown the capability to image single cells in vivo, but only for a few anatomical sites such as the brain and the liver; furthermore, MRI lacks sufficient temporal resolution to track single cells circulating in the bloodstream and homing to sites of infection or injury.
Of all the existing imaging modalities, PET has the highest molecular sensitivity and thus offers the most promising path towards tracking single cells in vivo and at the whole-body level. However, conventional algorithms used for reconstructing PET images are not optimal for tracking the trajectory of a single cell. The output of a conventional PET scan—large 3D images with millions of elements—is poorly suited for representing a single moving point source with high temporal resolution. This inefficient representation leads to an ill-posed reconstruction problem, meaning that millions of image elements must be computed from a small number of recorded PET coincidence measurements. Furthermore, a sequence of tomographic images is inefficient at representing the continuous motion of a sparse set of sources because it implies a discretization of space and time. As a result, PET images reconstructed from low-activity point sources are noisy and not suitable for tracking a moving source. A few strategies have been proposed to reconstruct dynamic PET images that are continuous along the temporal dimension, but these methods still represent the spatial dimension as a matrix of discrete elements. Alternatives to conventional image reconstruction for tracking single positron-emitting sources using PET have been proposed and investigated, especially in the field of chemical engineering. Early studies have shown that single particles labeled with a positron emitting radionuclide can be used as tracers to analyze complex patterns of fluid and powder flows in chemical processes. The technique was later refined and became known as positron emission particle tracking (PEPT). Unlike PET, PEPT uses a minimum-distance algorithm to localize a single moving source directly from PET measurements, that is, without reconstructing a tomographic image. Because the radiotracer is confined to a single particle, the reconstruction problem can be reformulated as a localization task and the position of the particle can be estimated directly from raw PET measurements, by finding the point in space that minimizes the average distance to the recorded coincidence lines. PEPT further uses an iterative algorithm to reject scattered and random coincidences. By splitting coincidence events into temporal frames, PEPT makes it possible to estimate the time-varying position and velocity of a single moving particle. PEPT has been applied to a variety of problems in the chemical engineering world, including mapping the dynamic behavior of opaque fluids, and the flow structure in fluidized beds and in canned foodstuffs. A variation of PEPT has also been developed to simultaneously track two independent particles.
In most PEPT studies, single particles are labeled with 1-30 MBq of 18F and tracked using a dual-panel PET camera. However, it is likely impossible to label single cells with such high levels of activity. What is needed is a method of tracking single cells in vivo that can provide robust positioning using a reduced number of detected counts.